9-5-2011_Mon_rtf_2.33pm_Class_Action_Sex_Discriminatio= n_Prevention_Controls_of_AIS_FACULTY.rtf
Versions
9=
-5-2011_Mon_3.41pm_Geneder Diversification Anti
Geneder Diversification Anti Sex Discrimination Accounting Information Systems
(AIS)<=
span
style=3D'color:black;mso-style-textfill-fill-color:black;mso-style-textfill=
-fill-alpha:
100.0%'>: A Forensic Audit, Expert Witness Testimony &
Computer Litigation Support Empirical Case Study
Avi Rushinek, Sara Rushinek
Correspond=
with
Sara Rushinek, a=
rush@miami.edu
***
*** Some of the tables=
have
been omitted due to space constraints. Please contact the authors in writing
for more information.
=
I Abstract
This
paper describes a research proposal, encompassing forensic accounting syste=
ms
related to gender diversity. It is=
a theoretical
and practice-oriented paper. It de=
als
with Accounting Information Systems (AIS) Academic Curricula and research, =
Pedagogy
of data collection and Diversity in Business and in Higher Education. Specifically it discusses Gender Issues,
History, of anti-discrimination against AIS female faculty members’ promoti=
on
and tenure decisions.
In addition =
to using
the traditional empirical statistical methods, this study proposes a new
real-time forensic surveillance audio video audit systems, repurposed for
distance & blended education, as anti-discrimination preventive
controls. Together with social med=
ia,
& Search Engine Optimization (SEO) domination it can promote gender
diversification and regulatory compliance.[i]<=
![endif]> As some of the American Accounting
Association (AAA) publications indicate such topics are becoming more and m=
ore
popular.
II Introduction
The authors develop a methodology to control for “Class Action Sex
Discrimination” law suits using Accounting Information System (AIS) faculty
promotion as a case in point. This=
is a forensic
audit, expert witness testimony & computer litigation support case study
approach to minimize the monetary damage, flag emerging cases and providing
quick settlement of a class that is pending.
Such systems can be used by accreditation organizations such as the
AACSB, who accredits accounting programs and examined how their publication
rate. Academic organizations such =
as the
American Accounting Association (AAA) can use it to promote equal opportuni=
ties
and compliance with the Equal Employment Opportunity Commission (EEOC). The EEOC can use it, to enforce complia=
nce,
and it can be used by universities and law firms as well...
Using standard statistical methods, the authors found that universiti=
es
promote AIS faculty primarily on the basis of publications as they were
statistically significant. In cont=
rast,
the “gender” or sex was statistically insignificant, but indicate a bias in
favor of female faculty members in the sample.
This means that there is no evidence of sex discrimination in the
population, but the sample suggests that universities may be biased against
males and in favor of female faculty members in promotion decisions. The methodology can be used as red flag=
s to
investigate possible inequities in promotion decisions.
The proliferation of “Class= Action Sex Discrimination” cases reported by the press raises the issue of preventative controls. This study looks at the case of AIS fac= ulty promotion from a forensic audit, expert witness testimony and computer litigation support point of view. =
Class action sex discrimina= tion suits have been discussed recently in the press. For instance Anthony Sebok (2004) reported that the class action sex discrimination suit against Wal-Mart was certified a sex discrimination cla= ss action suit. When a court "certifies" a class action, it allows the case to proceed with a class of plaintiffs, according to class action rules - as opposed to with a number of individual plaintiffs.) The class is claiming that Wal-Mart discriminated against women in the way it recruited and promoted managers.
A nationwide employment discrimination class action lawsuit has been filed against Costco Wholesale Corporation (Patel, 2007)... The suit charges that Costco operates a "glass ceiling" at the store-management level which precludes wom= en from obtaining promotion to assistant manager and general manager positions= .
Wong (2008) reports about an= other similar case of gender discrimination lawsuit against Smith Barney. The plaintiffs alleged that Smith Barney discriminated against female Financial Advisors in compensation and business opportunities. The parties entered into a four-year settlement agreement th= at is subject to Court approval.
Universities may also be at = risk for possible class action suits. I= nstead of institutions being in a reactive posture they may want to institute more preventive measures and continuously evaluate how well they abide by these measures.
This study uses the number = of refereed publications as a measure of research productivity. The authors develop a model to forecast= the faculty rank (1 assistant, 2 associate and 3 full professor ) as a function= of the number of publications and the faculty gender (Sex=3D1 for males, and 0= for females). Likewise, this model off= ers a methodology of testing for Sex discrimination by the significance of the Sex variable on rank promotion decisions. If the Sex is statistically significant, then discrimination may exist in the population. In contrast, if Sex is statistically insignificant, then discrimination is less likely in the population. If it appears like discrimination exists, then the plaintiff may need to set up a class-action server and let people join the class. We are developing such a server with a survey to support such class-action law= suits based on earlier work (Rushinek, 1990, 1997, 2005).
The purpose of this study i= s to develop a regression analysis connected to a continuous online survey that = will raise red flags for the existence of sex discrimination. This is a tool for compliance forensic = audit with regulations of government agencies, such as the EEOC in the United Sta= tes... The regression helps determine whether = sex is a significant factor in promotion decisions. If it is significant, then it may indicate that a class action may f= orm at a future time, and enable the parties to reduce its likelihood of occurr= ence (Rushinek, 1993, 2000).
We are using data collected= by Simkin (2007) but applying it in a different way, and suggesting another data collection methodology for the future. Unlike Simkin et. Al. who focus on the “profile of the publication r= ates of accounting faculty, and gender” for academic purposes, we are looking at= it more from the forensic audit, expert witness testimony, and litigation supp= ort point of view.
Thus, our goal is to create benchmarks that might be useful to decide whether gender or sex discriminat= ion exists on a continuous basis in an automated cost effective method of an internet WWW server online survey methodology. For this purpose we recommend using Microsoft SharePoint software that lets a lay person such as an administrator that is not an IT (Information Technology) professional to de= sign and implement online interactive continuous survey, quickly and effectively (Rushinek and Rushinek, 1987, 1989, 2005).
III Review of the
Literature of Research Productivity
We have reviewed numerous applications and selected the best for
evaluation of regulatory compliance such as the compliance with the US EEOC=
. =
What we are using here is a discrimina=
nt
model, a model that discriminates between significant and insignificant sex
discrimination. T=
his
is based =
on
earlier studies by Rushinek and Rushinek (1985,
2000) t=
hat
purports that the forensic auditor can provide effecti=
ve expert witness testimony and com=
puter
litigation support regard“AIS Sex discrimination class action law suits.
Quick system response time is the main determinate of overa=
ll
user satisfaction. (Rushinek, 1987, 1989).
If filling out the survey takes too much time, users will fail to fi=
ll
it and the data will not be accurate.
This study deals with anti-discrimination compliance s=
oftware;
it
is an interactive online expert
system using diagnostic audit trails through telecommunication networks.
We = focus on providing expert witness testimony and computer litigation support for employment and labor law discrimination cases, in general and class actions= in particular. There are many other r= easons why measures of research productivity are important to accounting faculty.<= span style=3D'mso-spacerun:yes'> As Simkin et al. (2007). suggest that i= t is important “in creating benchmarks for the quantity and quality of publicati= ons needed to obtain tenure, be promoted, or receive annual merit awards (Hasselback, et. al, 2000; Hult, et. al., 1997, Pierce and Garner, 1995; Hagerman and Hagerman, 1989) Then too, administrators have a similar intere= st in these norms (Maranto and Streuly, 1994; Campbell, et. al., 1983; Schultz, et. al., 1989).
Fac= ulty research productivity is also important to those universities and colleges seeking accreditation (Bublitz and Kie, 1984; Hasselback, 2000). One application in the accounting area = is for comparisons among similar institutions (Lucertini, 1995; Posy and Parker, 1= 989; Jacobs, et. al., 1986), comparisons between Ph.D. and non-Ph.D. granting institutions (Schultz, et. al, 1989), or studies seeking to identify the accounting profession’s most prolific researchers (Hech and Bremsyn, 1986; Hech, et. al, 1991; Gopolakrishan and Chandy, 1991).” Some research correlates publications t= o the area of specialization. (Campbell and Morgan ,1987; Englebrecht, et. al.,1994).
Gender studies of research productivity have been common, but= none of them focus on sex discrimination and compliance testing. As Simkin et. al. suggest “Cole and Zuckerman (1984) note, for ex= ample, that over 50 such studies of academics outside the accounting discipline ha= ve found that female faculty members publish significantly fewer articles than their male counterparts. In a later study, Zuckerman (1987) found that women publish between 50 and 60 percent as many papers of what men publish.” However, for the purpose of class action employment litigation is it significant?
Gen=
der
differences apply to the accounting areas as
Streuly
and Maranto [1994] found that men published slightly more articles, but that
these differences were not statistically significant. Dwyer [1994] confirmed the finding acad=
emics
(per Hasselback’s Accounting Faculty Directory) that women had publi=
shed
fewer times in academic journals, but is it statistically significant and
up-to-date.
As schools increase the publications
requirement for promotion (Read, Rama, and Raghunandan, 1998; Zivney, 1995;
Milne and Vent, 1987, Englebrecht, et. al, 1994), testing for sex
discrimination may be more important. This study focuses on “premier”
journals articles (Van Fleet, et. al., 2000, Hasselback, et. al., 2000).
The quality of a journal is= a subjective matter and varies among researchers (see, for example, Zivney, e= t. al., 1995). AIS faculty member oft= en publish in computer publications, which may not be considered as Accounting Journals. As Daigle and Arnold (20= 00) found that the “top 25 AIS researchers” listed non-accounting publication outlets as the top journals. Likew= ise, Christiansen, et al., (2002) suggests that the tendency applies to accounti= ng Ph.D.’s in that study’s researcher cohort.
Adjusting for co-authors (Hasselback, et. al., 2000) or accounting specialty (Campbell and Morgan, 1= 987) may further confound the results (Alexander and Mabry, 1994; Brown and Gard= ner, 1985). “Leveling the playing field” among the research facilities of organizations may further confound the finding (Englebrecht, et. al, 2000, Read, et. al., 1998, Dwyer, 1994, Schultz, et. al., 1989
IV Microso= ft SharePoint Online Survey - Advantages Of an On= line Compared To a Traditional Survey <= /p>
As = Simkin et al. (2007) note “many of the studies cited above are now at least ten ye= ars old or used statistical methods that some scholars suggest hide more than t= hey show.” The trouble with paper surv= eys is that they are so expensive, and time consuming. Traditional survey also involves many un-needed intermediaries that = slow down the process. Such intermediar= ies as chairperson of the accounting department at each school are “requesting that this individual in turn distribute them to the faculty members in their departments,” which is yet another intermediary in the process of data collection. Furthermore, such surv= eys are typically based on printed directories, such as “faculty and school lis= tings in Prentice Hall’s Accounting Faculty Directory 2001-2002 (Hasselback’s),” which may be outdated as soon as they are published. In contrast, schools update their web s= ites much more frequently than any printed publication. Therefore, using an online survey that = picks the information from the internet WWW can be more up-to-date than a printed directory (Rushinek 2005, 1997).
We have used the data that = Simkin et. al. (2007) collected as follows: “The areas of research specialization = were (1) accounting information systems (AIS), (2) auditing, (Audit), (3) financ= ial accounting (Financial), (4) managerial accounting (Management), (5) tax accounting (Tax), and (6) other (Other). The Other category captured all responses that did not list the first five areas above, and included governmental and not-for-profit accounting, international accounting, accounting education, and ethics. Our academic rank categories were (1) Adj= unct Professor, (2) Assistant Professor, (3) Associate Professor, (4) Full Professor, and (5) Other.”
We have focused on AIS (Acc= ounting Information Systems) faculty member’s ranks, Assistant, Associate, and Full professors, their number of publications, and whether they were males (1) or females (0). This way we had a muc= h more homogeneous data, which would result in a higher R-Square in a multivariate linear regression analysis.
Counter Party Audio/Video (AV)
Surveillance Audit Literature Review
This study develops a multidiscipl=
inary
Expert Witness Testimony (EWT) Information Technologies (IT) and Search Eng=
ine
Optimization (SEO) demonization methodology.
Traditionally, IT had difficulty integrating AV into the standard Ex=
pert
Witness Testimony and Computer Litigation Support (EWT-CLS). More recent social AV networks, like Yo=
uTube,
have facilitated the integration of EWT-CLS, SEO, as the literature shows
(Rushinek and Rushinek 2009 April, August, 2010 September 2010a-e). Such
systems can use Counterparty
Surveillance of Collateralized Debt Obligations, in different industries su=
ch
as Oil and Gas, Education, all of which reduce cost and raise ROI
Mobile telecommunication, such as =
Cell
Phone (iPhone) Real Time applications (Apps) apply Forensic Audit to e-Bill
(Electronic Billing) Records over the WWW (World Wide Web) AV IP (Internet
Protocol) Network Security Surveillance Reconnaissance PTZ (Pant, Tilt, &am=
p;
Zoom) Cam Systems, CCTV (Closed Circuit TV) & DVD-Recording. These are repurposed from Stand-Up
Instructions for Distance or Blended Education and CPE (Continuing Professi=
onal
Education). In the Health Care, Fo=
rensic
Information Technology Networks of Expert (FITN) Witnesses Teams are resear=
ching,
consulting, & auditing using Employer
Discrimination Compliance Analysis/Alert System and the Convergence of Fore=
nsic
Experts in Accounting and Computing.
This study focuses on assessing punitive damage awards, upheld by the
courts, when academics retaliate & defame, voilating Anti
sex-discrimination EEOC Gender compliance regulations. This study develops Forensic WWW Audit
systems of Salary Forecast Class Action Lawsuit. Such Automated Diversity Compliance
Real-Time AV Audit for Network Security, can also be used for Disaster
Planning, Backup & Recovery. It
helps with Costs Reduction Sponsor Ad Funding, e-Commerce Podcast Revenues
generation from Digital Media Streaming as an additional preventive
anti-discrimination control. The s=
ame
systems can be used to automate
Software for Accounting, Teaching, and Recruiting.
V Data Analysis And Results:
We ente=
red the
information from the Simkin et. al. (2007) into Microsoft SharePoint server
survey.
The Publication Rates Of Ac= counting Faculty: A Multidimensional Profile Publication Rates of Accounting Faculty= : A Multidimensional Profile divided the “survey instruments into a computerized data set. Auditing of the entered data resulted in an approximate error rate of .2% for the 15% of the data subjected to random review. The two data-entry errors found were corrected.” Their data “provides m= ean publication rates for all faculty combined, separated by area of research specialization and gender. These results reflect the reported number of peer-reviewed publications divided by the number of respondents reporting t= hat specialization. Thus, we grouped the data into 6 groups of only AIS faculty= by gender, and rank of assistant, associate, and full professors.
Table A
|
|
Table
A |
AIS
Rank=3Df(Pubs,Sex) |
|
|
|
|
|
|
|
|
Regression
Statistics |
|
|
|
|
|
Multiple
R |
0.971985 |
|
|
|
|
|
0.944755 |
|
|
|
|
Adjusted
|
0.907925 |
|
|
|
|
Standard
Error |
0.271403 |
|
|
|
|
Observations |
6 |
|
|
|
The SUMMARY OUTPUT in Table= A describes the AIS rank as a function of Pubs (Publications) and Sex (Male or Female), AIS Rank=3Df(Pubs,Sex).
=
Regression Statistics inclu=
de the
Multiple R 0.971985,
The Regression Statistics s=
how the
Multiple R with a value of 0.971985, and the
Following is a description = of the faculty rank values and their position descriptions:
Rank Legend
Rank Descrip= tion of Rank
1 Assi= stant Professors
2 Asso= ciate Professors
3 Full Professors
Table B
|
ANOVA |
|
Table
B |
AIS
Rank=3Df(Pubs,Sex) |
|
|
|
|
|
|
|
Df |
SS |
MS |
F |
Significance
F |
|
|
|
|
Regression |
2 |
3.779021 |
1.88951 |
25.65186 |
0.012985 |
|
|
|
|
Residual |
3 |
0.220979 |
0.07366 |
|
|
|
|
|
|
Total |
5 |
4 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Coefficients |
Standard
Error |
t
Stat |
P-value |
Lower
95% |
Upper
95% |
Lower
95.0% |
Upper
95.0% |
|
Intercept |
1.034536 |
0.206693 |
5.005184 |
0.015349* |
0.376747 |
1.6923249 |
0.376746971 |
1.69232486 |
|
Pubs |
0.085692 |
0.011964 |
7.162661 |
0.005605* |
0.047618 |
0.123766 |
0.047618181 |
0.123765976 |
|
Sex |
-0.359907 |
0.227225 |
-1.58392 |
0.211378** |
-1.083039 |
0.3632256 |
-1.08303909 |
0.363225632 |
|
|
|
|
|
|
|
|
|
|
- * Significant at t= he .o5 level of statistical significance
- ** Not significant at the .o5 level of statistical significance – Less likely to have a Class Action Sex Discrimination Case!
The ANOVA (Analysis of Var= iance) in Table B support the summary in Table A. It shows the statistical significance of the model of AIS Rank=3Df(Pubs,Sex). Table B includes the following paramete= rs: df (Degrees of Freedom), SS (Sum of Squares), MS (Means Squares), and the F va= lue and the Significance F. For the Regression, these param= eters have the following values of: 2, 3.779020662, 1.889510331, 25.65185978, and 0.012984867, respectively= .
Most importantly, the last= 2 values reflect the test of hypothesis, rejecting the null hypothesis that entire m= odel have zero correlation with rank with an F-value of 25.65185978 and a statistical level of confidence of 0.012984867, which is better than at the= .05 level of statistical significance.
In contrast with the F and=
the Significanc=
e F
for the entire model, only some of the individual variables are
significant. The first two namely =
the
intercept and the Pubs are significant, but the Sex is not significant at t=
he .05
level. The second part of this ANO=
VA
test shows the Coefficients, Standard Error, t Stat, and P-value, for the Intercept 1.034536, 0.206693, 5.005=
184,
0.015349 and the Pubs 0.085692, 0.011964, 7.162661, 0.005605 respectively.
Most important are the pairs of t Stat and
P-value for each of the variables. The
P-values show the statistical significance being lower than the .o5 require=
d to
reject the null hypothesis, saying that in the population these variables a=
re
greater than zero, and therefore, statistically significant in making rank
promotional decisions. It suggests=
that
the number of publications is a significant factor in promoting assistant to
associate, and associate to full professor of AIS.
However, for the last variable Sex, the
opposite is true, it is insignificant at the .05 level. The Sex, a dummy variable (1 for male and 0 =
for
female) has the following information after the Pubs in the table, it shows
the: Coefficients, Standard Error, t Stat, and P-value: Sex=3D-0.359907,
Another important piece of information is the Coefficients of Publications and Sex. These coefficients reveal that on the a= verage the Pubs with a value of 0.085692079, implies that it takes 1 publication p= er rank. Or, in other words, as an As= sistant professor would need another publication over and above the Intercept of 1,= or about 2 publications to be ranked as an Associate professor and an Associate Professor may need about 1 additional publication to be ranked as a Full Professor.
The Sex coefficient with a= value of Sex=3D-0.35990673, is the most interesting from the EEOC compliance stand point. The negativity of Sex shows= that a male with a data value of 1 incurs a penalty compared to a female with a value of 0, in terms of the contribution of their publications to the promotion. Or in other words, on t= he average male AIS faculty members may need more publications to be promoted compared to their female counterparts. It may reflect the fact that in the sample female faculty member published less then male faculty members of the same rank. However, it is not sufficiently statist= ically significant to generalize it to the entire population, and therefore, we can conclude that there is no evident of sex discrimination in promotion decisi= ons for AIS professors, in the population.
Table C
|
F-Test
Two-Sample for Variances |
Table
C Pubs Differs Between Sexes |
|||||
|
|
|
|
|
|
|
|
|
|
Pubs |
Sex |
|
|
|
|
|
Mean |
13.36667 |
0.5 |
|
|
|
|
|
Variance |
108.2187 |
0.3 |
|
|
|
|
|
Observations |
6 |
6 |
|
|
|
|
|
df |
5 |
5 |
|
|
|
|
|
F |
360.7289 |
|
|
|
|
|
|
P(F<=3Df)
one-tail |
2.18E-06 |
* |
|
|
|
|
|
F
Critical one-tail |
5.050329 |
|
|
|
|
|
|
|
|
|
|
|
|
|
- * Significant at t= he .o5 level of statistical significance
F-Test Two-Sample for Vari= ances in Table C shows how Pubs differ between Sexes. Thus, it shows that we can reject the null hypothesis saying that ma= le AIS faculty members do not have a significantly higher publication records comp= ared to female AIS faculty members. We = reject it in favor of the alternative hypothesis, stating that male AIS faculty members tend to have a larger number of publications, per rank (assistant, associate and full professors). In= other words, the model can discriminate between the publications based on the sex= of the AIS faculty member as there is no significant difference among male and female AIS faculty publications, in favor of the F value of 360.7288889, using a P(F<=3Df) one-tail test with a value of 2.17647E-06, whic= h is way lower than the F Critical one-tail value of 5.050329058. Therefore, it is significant at the .05= level of statistical significance or better.
Just because discriminatio= n may exist in the model, it may not exist in the population. Further, even if there is discriminatio= n in the population, it does not mean that it is illegal. For example, it is possible that male A= IS faculty members have more publications, indicating reverse discrimination, = or discrimination against the male populations, which may not be illegal, as opposed to discrimination against the female population, which may be illeg= al. =
=
VI Data Collection
Thi= s is a description of how data can be collected to continuously test for regulatory compliance, such as Sex discrimination compliance with the EEOC. The Microsoft ® SharePoint ® server will collect the data, regress the Ra= nk promotion on Sex, test the significance and issue a warning as soon as it f= inds statistically significant Sex affect on Rank.
Screen Shot 1
Screen Shot 1 describes how= to setup the survey online for a lay-person (not an IT professional), such as a paralegal, a forensic auditor etc. After selecting from a menu, the option to display a survey the user will start picking up questions. In step 6, the user will pick multiple choice questio= ns. In our case, the system will apply to t= he rank of the professor that joins a class or responds to the survey. Thus, the question will read as follows= :
What is your rank?
-
Screen Shot 2 shows some of= the advantages of an interactive online survey over a traditional survey, which= is the branching capability. As the instructions suggest that branchin= g lets you simplify your surveys for your respondents by displaying only questions that are relevant to each respondent. Questions that are not relevant are n= ot displayed. The relevance is determined by the respondent’s answer to the preceding question. For example, y= ou might have the following questions in your survey:”
For our survey, we will as the following:
-
Screen Shot 3 shows another advantage of an online survey, the ability to preview the survey and find o= ut if the survey developer made mistakes, and correct them ASAP. Likewise, the user can create an e-mail message and include hyperlinks, as the instructions suggest: “After confirm= ing that your survey operates as intended, send the survey. Rather than sending= an e-mail message with a link to the home page of a team site, you can send a = URL (Universal Resource Locator) that takes survey recipients directly to the survey.” Entering the information directly into = the survey by-passes the problems with e-mail delivery that can be delayed and misclassified as “junk mail,” never reaching its destination. Thus, we “create an e-mail message, and paste the survey address into the message. Your respondents will be able to click the link to go directly to the survey.”
Screen Shot 4
= Screen Shot 4 shows some instructions: “When drafting the e-mail message to survey recipients, be sure to include the following: 1) a concise description of w= hat data the survey will collect, 2) an explanation of how this data will be us= ed, 3) how long the survey will take to complete, and 4) whether the survey is anonymous.” In our case such a tip= is very important since most people disregard the survey as a waste of time and fail to respond, or fail to respond accurately. So it is important to explain the rationale for the survey as follow= s:
1) A concise description of what data the survey will collect: this an EEOC anti-discrimination class action suit survey,
2) An explanation of how this data will be used: the d= ata will be used exclusively for testing whether you are entitled to get paid d= ue to illegal Sex discrimination against you (a respondent maybe more likely to respond knowing that it is not just an “academic” survey,
3) How long the survey will take to complete: this sur= vey will take only 5 minutes to complete , and
4) Whether the survey is anonymous: we need to get your information to decide whether the organization discriminated against you, a= nd we need to verify it (most of it is available publicly such as Sex, Publications, and Rank) so it cannot be anonymous.
Another advantage is the graphical summary of responses. An example of a graphical summary is shown which helps the respondents avoid mistakes as they can see graphically their responses and how it compares to= the average of all other responses. So= for example, when we ask for a number of refereed journal articles, of top jour= nals, a faculty member will not enter journal not listed on a hyperlinked “top journals” list, and overstate their publication rate unintentionally. Otherwise, we may have to remove outlie= rs of faculty members that say that they have published several hundred articles.= .
Most important advantage is= the instant and programmatic export to Microsoft Excel, which can calculate the regression analysis, and issue an alert based on predetermined conditions of deviations from the standard. Much= like management by exception, we can get an alert only in the case that the variance is significant, exceptional, and warrants additional rapid action.
“The overall mean publicati= on rate was 15.4 articles per capita. This means that that the average accountant in our survey, regardless of rank, years of service, or gender, had published = over 15 blind-refereed articles to date.” Outliers with publications in excess of 100 blind-refereed publications.” Simkin et. al. also= “notes that male respondents had an overall mean rate of 18.0 publications per per= son, compared to female respondents of 8.6 publications. This gender differential is consistent = with earlier studies, and exactly mimics Zuckerman’s findings that women publish= about 50 percent as much as men do (Zuckerman, 1987). But is it possible that there are a relatively larger number of fema= les in the lower academic ranks, and perhaps have merely lacked the time to publish?”
They ” also indicate that t= he mean number of publications reported for all male faculty members (i.e., in the columns labeled “All”) was higher at each rank than for female faculty. For example, the mean number of publications was 4.7 for male assistant profess= ors compared to “2.7” for female assistant professors. The values for associate professors were “12.6” (for males) versus “8.5” (for females), and for full professors was “29.0” (males) versus “22.6” (females).
These results suggest that = the gender differential in mean total publications… cannot be explained by the = fact that a higher percentage of the females answering our survey happened to be assistant professors with a smaller number of publications. We also note that these gender differen= ces appear to apply across the sub-disciplines of accounting, with the notable exception of AIS Assistant professors .. in which females out-published males. However, the number of respondents .. is small (5 respondents in total for the AIS category) making generalizations difficult.
A number of factors are lik= ely to affect the publication rates of accounting faculty. One of the most important is employment longevity—i.e., the length of time that has passed after receiving a doctor= ate. To ascertain whether the lower numbers = of publications reported by female accountants is indicative of the fact that women have only recently had increased numbers in the profession, we scaled= the number of publications reported by the number of years since the respondent completed his or her doctorate.”
Further they find that “the research outputs of the various accounting specialties are inconsistent acr= oss the sub-disciplines.” Therefore, i= t may be important to focus on each specialty separately, rather than mixing them= up. Accordingly, we have focused on AIS, to= avoid mixing incomparable accounting sub-specialties, such as the following: AIS (Accounting Information Systems), Audit, Financial, Managerial, Tax and oth= er Accounting sub specialties.”
VII Future Studies
Future studies should supplement the self-reported data, with verification of publ= icly available data on the Internet WWW (World Wide Web). We can easily verify the answers to que= stions dealing with whether a school recently installed or discontinued a Ph.D. program. Smaller sample sizes shou= ld be much easier to expand if the entire process is conducted automatically on a= web server over the Internet It shoul= d be cheaper, faster, and less error prone, thus more accurate.
Most importantly, an online interactive survey lets us constantly update it and bypass intermediaries t= hat slow down the process, and make it more error prone. Likewise, using a web server and e-mail communication is a much more effective and less costly compared to a traditional paper based surveys. So in the future it should become more popular as the technology improves and becomes more users friendly.
Also, the data can be verif= ied (as Simkin et. al. suggest) “by looking at adjustments for “co-authorship” in i= ts research productivity measure…“research quality”— academic and practitioner-oriented journals, hard-copy versus electronic journals, peer-reviewed versus internally-reviewed publications, or journals whose policies first require conference presentations at regional or national meetings.”
VIII Summary, Conclusion= s And Implications
Class A= ction Sex Discrimination law suits have become more prominent and more costly in recent years. Therefore, their Prevention Controls may also become more important. We have looked at the case of AIS faculty promotion from a forensic a= udit, expert witness testimony & computer litigation support stand point.
In summ= ary, we have performed a multiple linear regression and developed a model that forecasts promotions of AIS faculty as a function of Publications and Sex.<= span style=3D'mso-spacerun:yes'> We have demonstrated that the model is accurate and reliable for AIS promotional decisions, and it is statistically significant. Likewise, we demons= trated that while the model is significant the Sex variable by itself is not significant, and that is a sort of a test for compliance with anti-discrimination regulations such as the EEOC. We also suggested a different survey methodology, instead of the traditional paper survey, we suggest an online survey running on a WWW server, using Microsoft SharePoint software to continuously update the results automatically.
In conc= lusion, we found that at the present time it appears that there is no evidence of discrimination in the population of AIS faculty promotion, but this may cha= nge in the future. We confirmed previo= us finding of differences in publication between the genders. However, these differences were not statistically significant and therefore cannot be generalized to the population.
The implications of our study is that in a future time Sex discrimination may become a problem and class action law suits may spread into the academic environment. For that reason we su= ggest automating the process of data collection and data analysis that will help = flag risks of compliance and the absence of compliance with regulatory enforceme= nt.
Such on= line interactive continuous surveys and regression analysis of the results can b= e an effective internal control and a preventive control for regulatory compliance. Likewise, it can also = serve as a method to determine whether or not discrimination exists, and if it do= es what is the size of the bias in favor on one sex and against the other sex.=
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Rushinek, A. and Rushinek, S. "Training Software Case Stu= dy: A Product Evaluation and Selection System", Journal Of Education For Business, 1989
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Rushinek, A. and
Rushinek, A. and Rushinek, S. "Project Management Software Feature Profitability: Windows, Networks, Mainframes, Filtered Task Diagram= s, Schedules, and Calendars, Journal Of Computer Information Systems (JCIS), Vol. 37, No. 4, Summer 1997, 48-55=
Rushinek, A. and S. Rushinek “Internet Fraud Auditing: A Simulated Health Care Industry Case Stud= y”= - Journal of Forensic Accoun= ting, 2000 - rtedwards.com
Rushinek, A. and S. Rushinek “What Makes Users Unhappy: Share-Point Team Services Web Server Securi= ty” Ubiquity, Volume 6, Issue2, = Jan. 25 - Feb. 1, 2005.
References
Rushinek, A. and Rushinek S. (2010= a) “Uncooking The Books From Toxic Paper Sub-Prime Mortgages CDS And CSOS Material Misstatements Of The Financial Services Industry: Crisis Challenges And Counterparty Surveillance Of Collateralized Debt Obligations”, The International Journal of Economics and Accounting, Volume 1, No. 1/2, 2= 010, 138-160. h= ttp://www.inderscience.com/storage/f712811263411059.pdf
Rushinek, A. and Rushinek S.(2010b, June) “Oil ETN Trading Rule Linear & Polynomial Daily Open & Close Regression Forecasts- An Automated Software Development- E= xit Strategy And Automated Stop Rules”, Oil, Gas, Energy Quarterly, June 2010, Volume 58, No. 4,683-698.
Rushinek, A. and Rushinek S. (2010= c) “Assessment Automation Software for Acco= unting of Domain Name Auctions, Forensic Audit Appraisal, ANOVA Of Price And Cost,= ROI Univariate And Multivariate Linear Regression Forecasting Models”, International Journal of Managerial and Financial Accounting, accepted for publication, Issue 4, 2010.
Rushinek, A. and Rushinek S.(2010d) "Forensic-Free AV Surveillan= ce Audit Podcasts For E-Competence Of Academics And Staff – Repurposed Traditi= onal Instructions For Blended, Distance And Self-Study E-Learning To Maximize Lecture Capture ROI", I= nternational Journal of Continuing. Engineering Education and Life-Long Learning, Vo= lume 20, Nos. 3/4/5 2010, 430-441. h= ttp://www.inderscience.com/search/index.php?action=3Drecord&rec_id=3D37= 057&prevQuery=3D&ps=3D10&m=3Dor
Rushinek, A., Rushinek, S. and Stutz, J. (2010e) "An Automated Internet Analysis/Alert System For Employer Compliance With EEOC/ADA Discrimination Regulations", accepted for publication in the Journal of Forensic a= nd Investigative Accounting, Se= ptember 2010.
Rushinek, A. and Rushinek S. (2009, April) "Tele-Medicine's SMART Surveillance A= V HD IP Security Network Camera Audit Systems," Jay Weiss Center for Social Medicine and Health Equity, University of Miami Miller School of Medicine, 2009 JWC SMART Forum, Lois Pope LIFE Cente= r, April 24,2009, Miami, Florida.
Rushinek, A. and Rushinek, S. (200= 9, August) ‘IFRS compliant real-time 2-way AV (audio/video) HD (high definitio= n) recording audit of effective learning strategies for forensic IT networks of expert witnesses research that focuses on investigative accounting testimon= y, & computer litigation support, effective learning strategies’, 2009 American Accounting Association (AAA)Annual Meeting, 3 August 2009, New York. Available online at:
h= ttp://aaahq.org/AM2009/ELS01.cfm
(Accessed on 27 October 2009).
Rushinek, A. and Rushinek, S. (200= 9, August) ‘Cell Phone Real Time Forensic Audit of Tele-Medicine e-Bill Records over the WWW AV IP Network Security Surveillance Reconnaissance PTZ Cam Systems, CCTV & DVD-Recording: Repurposed Stand-Up Instructions for Distance or Blended Education and CPE, Emerging and Innovative Research Session’, 2009 American Accounting Association (AAA) Annual Meeting, Tuesday 4 August 2009, NY. Available online at:
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Rushinek, A. & Rushinek, S. (2009, August)"Health Care Forensic Information Technology Networks of Expert (FITN) Witnesses Teams Researching Investigat= ive Tax, Accounting and Auditing (FIA) Forecasts Archives of Testimony, Assessm= ent, Publishing, Teaching, Service, and WWW Multi-Media Consulting" 2009 American Accounting Association (AAA) Annual Meeting NY, NY., Tuesday Augus= t 4, 2009, http://aaahq.org/AM2009/Emerging.cfm<= /span>
Rushinek, A., Rushinek, S., Stutz, J. (2010, August) “Auditing Employer Discrimination Compliance Analysis/ Alert System and the Convergen= ce of Forensic Experts in Accounting and Computing” Annual AAA Forensic= and Investigative Accounting Section Research Conference, Baton Rouge, LA. May = 7 - 8, 2010. http://aaahq.org/meetings/2010F= IA_program.htm
Rushinek, A. and Rushinek, S. (2010, August) “Assess= ing punitive damage awards upheld by the courts when academics retaliate defame-Anti sex-discrimination EEOC Gender compliance Forensic WWW Audit &a= mp; Salary Forecast Class Action Lawsuit RSS Controls- Multivariate Regression = of eMBA Rankings,” American Accounting Association Annual Meeting 2010, San Francisco, CA., August 4, 2010.
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Addendum I
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Denver 2011 American Accounting Association Annual Meeting, Monday Augu= st 8, 2011 — 9:45 am-11:00 am
http://aaahq.org/AM2011/ELS01.cfm
Mobile Cell Phone Repurposed Surveillance Audit HD Video for Effective Learning Strategies of YouTube's Innovative Social Media Networks for Wirelessly Auto-Sharing, Tagging, Embedding, Syndicating, Captioning, Subtitling, & Machine-Transcribing by Google
Presenters: Avi Rushinek, University of Miami; Sara Rushinek, Universit= y of Miami;Description: Users disseminate successful practices of the first course sequence in accounting that piqued their interest by FaceBook®, Twitter®, Reader®, Orku= t®, MySpace® about accounting and pursuit of the CPA profession as a career. The session stresses those changes in content segment or a subset of skills rat= her that a programmatic or curricular changes.
Search Engine Optimization (SEO) & Domination Audit for Effective Learning Strategies of World Wide Web's Internet Sites: Innovative Methods for Teach= ing, Capturing, Storing, Distributing Lectures via Automated Uploading to Ad Hoc Network Hot Spots
Presenters: Avi Rushinek, University of Miami; Sara Rushinek, Universit= y of Miami;Description: Ad Hoc hot spot Lecture Captures (LC) to anywhere. Streamline LC workflow. Methods to automatically free up space once your lectures have been safely delivered. Upload on-the-go by personal Wi-Fi Hotspots. Automatically uploa= d, geotag, back-up wirelessly share your lectures. Lectures are indexed and ca= n be searched via the web.
http://aaahq.org/AM2011/KC_program_PublishBySection.cfm?sectionID=3D8
Wednesday August 10, 2011 — 4:00 pm-5:30 pm
9.25 Methodology to Control for Class Action Sex Discrimination Law Suits
(NASBA Field of Study: Accounting)
Moderator: Avi Rushinek, University of MiamiPanelists:
Sara Rushinek, University of Miami
-
[i] Rushinek, A. and
Rushinek, S. (2011, August) ‘Methodology to Control for Class Action Sex
Discrimination Law Suits’, AmericanAccountingAssociation AAA Annual Meeting
2011, Denver, Colorado, August 6-1=
0,
2011. Moderator: Avi Rushinek, University of Miami, Panelists: Sara Rushine=
k,
University of Miami, Accessed 9/5/2011: http://aaahq.org/AM2011/KC_program_PublishBySection.cfm?sectionID=3D8